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    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/17323

    Title: Cellular Neural Networks for Gray Image Noise Cancellation Based on a Hybrid Linear Matrix Inequality and Particle Swarm Optimization Approach
    Authors: 蘇德仁
    Keywords: Cellular neural networks;Particle swarm optimization;Linear matrix inequality;Noise cancellation;Image
    Date: 2010-09
    Issue Date: 2012-09-06 14:13:27 (UTC+8)
    Abstract: This paper describes a technique for gray image noise cancellation. This method
    employs linear matrix inequality (LMI) and particle swarm optimization (PSO) based on
    cellular neural networks (CNN).We use two images that one is desired image and the other is corrupted to find the CNN template. The Lyapunov stability theorem is employed to derive the criterion for uniqueness and global asymptotic stability of the CNN equilibrium point.The current study characterizes the template design problem as a standard LMI problem and
    the optimization parameters of the templates are carried out by PSO. Finally, the examples
    are given to illustrate the effectiveness of the proposed method.
    Relation: Neural Process Lett 2010, 32, 147-165
    Appears in Collections:[資訊工程系所] 期刊論文

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